Job Description

About This Role

The computational science group at Biogen is seeking a creative and self-motivated research fellow to design, develop, and apply new machine learning algorithms for the discovery of novel AAV capsids. The Research Fellow role at Biogen is an 18-month position designed to provide a unique opportunity for scientists to gain exposure to drug discovery and development and train in an industry research setting. In this role, you will collaborate across different functions within the BTMS organization to leverage machine learning algorithms to develop next generation AAV capsids.

What You’ll Do

  • Develop, train and test ML and/or bioinformatics methods that learn determinants of AAV capsid fitness for developability, stability, delivery and/or affinity, and use these to select candidates to carry forward.
  • Devise, train, refine, and deploy deep neural network models using public and proprietary sequence, structure, property or function data.
  • Collaborate closely with experimental scientists to create a model-test-learn loop.
  • Collaborate with internal stakeholders and with external (academia or industry) partners.
  • Develop and apply modeling and design strategies for proteins, antibodies or AAV capsids.
  • Keep abreast of state-of-the-art technologies in the field.
  • Present scientific findings to broad audiences including senior leadership to drive decision making in program teams.

Qualifications

Who You Are

The successful candidate will be a creative and highly motivated scientist with demonstrated expertise in the fields of computational biology, machine learning, or related fields. You will be expected to stay current with relevant literature and apply the latest advances in computational methods to address AAV capsid design and optimization challenges. As a key contributor to the computational science deliverables, thinking independently to solve complex problems is an important aspect of this position.

Required Skills

  • PhD in a field related to computational protein design such as Chemical and Biomolecular Engineering, Computer Science, Mathematics, Statistics, or Computational Biology.
  • 0-2 years of postdoctoral experience.
  • Experience with devising and implementing ML methods for protein, antibody or AAV capsid design.
  • Experience in applying deep learning methods that relate protein sequence, structure, property, or function (e.g., structure prediction, annotation, novel or variant sequence generation, property prediction, de novo design, directed evolution, etc.)
  • Strong communication, collaboration, and interpersonal skills
  • Strong publication record in peer-reviewed journals
  • Strong problem-solving and troubleshooting skills.
  • Legal authorization to work in the U.S.

Additional Information

Why Biogen?

Our mission to find therapies for neurological and rare diseases is a unique focus within our industry and this shared purpose is what connects us as a team. We work together to overcome obstacles and to follow the science. We are resilient as we strive to make an impact on our patients’ lives and on changing the course of medicine. Together, we pioneer. Together, we thrive.

At Biogen, we are committed to building on our culture of inclusion and belonging that reflects the communities where we operate and the patients we serve. We know that diverse backgrounds, cultures, and perspectives make us a stronger and more innovative company, and we are focused on building teams where every employee feels empowered and inspired. Read on to learn more about our DE&I efforts.

All qualified applicants will receive consideration for employment without regard to sex, gender identity or expression, sexual orientation, marital status, race, color, national origin, ancestry, ethnicity, religion, age, veteran status, disability, genetic information or any other basis protected by federal, state or local law. Biogen is an E-Verify Employer in the United States.

Location

Cambridge, MA, United States

Job Overview
Job Posted:
9 months ago
Job Expires:
Job Type
Full Time

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